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| Section | Weight | Objectives |
|---|---|---|
| Data Analysis | 30%-32% | - Perform advanced analytics using SQL
|
| Data Presentation and Data Visualization | 28%-29% | - Create dashboards
|
| Data Ingestion and Data Preparation | 15%-20% | - Perform data discovery to identify what is needed from available datasets
|
| Data Transformation and Data Modeling | 22%-30% | - Design data models
|
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NEW QUESTION # 31
You are working with a Snowflake table called containing sales transactions. The table includes columns like 'transaction_id' (VARCHAR), 'product_id' (VARCHAR), 'transaction_date' (DATE), (NUMBER(10,2)), and (GEOGRAPHY). You need to perform the following data preparation tasks: 1. Clean the data: Remove transactions with less than or equal to 0. 2. Transform the data: Convert the 'customer_location' from GEOGRAPHY to a WKT string representation. 3. Enrich the data: Calculate the year and month from the 'transaction_date' and add them as new columns. 4. Mask the data: Partially mask the 'transaction_id' column to protect customer data by only displaying the last 4 digits of the ID. Considering performance and best practices, what is the MOST efficient way to achieve these transformations using Snowflake features? You need to achieve this in a single statement.
Answer: B,D
Explanation:
Options A and D are functionally equivalent and correct, they both performs the desired data cleansing, transformation, and enrichment in a single 'CREATE OR REPLACE TABLE' statement. The 'WHERE sales_amount > clause filters out invalid transactions. RIGHT(transaction_id, 4) masks the transaction ID. YEAR() and MONTH() extract the year and month from the date, and ST_ASTEXT() converts the GEOGRAPHY object to a WKT string. Option B uses HASH, but the question asks for partially masking so HASH isn't suitable. Option C uses CASE, that is not required. Option E makes use of SYSTEM$MASK, function which masks the entire column value, that isn't what the questions asks.
NEW QUESTION # 32
Which Snowflake feature or object can be used to dynamically create and execute SQL statements?
Answer: A
Explanation:
While Snowflake provides various ways to encapsulate logic, Stored Procedures are the primary tool for procedural programming and dynamic SQL execution. Unlike standard UDFs, which are generally designed to calculate and return values within a query, stored procedures can execute administrative commands (DDL) and data manipulation (DML) that are not possible in a simple SELECT statement.
Using Snowflake Scripting (or languages like Python, Java, or JavaScript), an analyst can write a procedure that builds a SQL string as a variable and then executes it using the EXECUTE IMMEDIATE command. This is demonstrated in procedural logic flows where inputs determine specific execution paths. While Tasks can be used to schedule the execution of these procedures, the task itself is a scheduling agent and does not provide the logic-driven dynamic string construction inherent to the procedure.
NEW QUESTION # 33
How does Snowsight's ability to load different types of data impact data preparation for analysis?
Answer: C
Explanation:
Snowsight's capability to load different data types streamlines data preparation by supporting diverse data formats.
NEW QUESTION # 34
What option would allow a Data Analyst to efficiently estimate cardinality on a data set that contains trillions of rows?
Answer: C
Explanation:
When working with "Big Data" at the scale of trillions of rows, calculating an exact count of unique values using COUNT(DISTINCT column) is extremely resource-intensive. This is because Snowflake must keep track of every unique value encountered to ensure no duplicates are counted, leading to high memory usage and long execution times (often referred to as "spilling to disk").
To solve this, Snowflake provides HyperLogLog (HLL) functions. HLL(*) (or specifically HLL_ACCUMULATE and HLL_ESTIMATE) allows an analyst to estimate the cardinality (the number of unique elements) with a very small, known margin of error (typically around 1%). This is significantly faster and uses far fewer credits than an exact count because it uses a probabilistic algorithm rather than a state- heavy tracking mechanism.
Evaluating the Options:
* Option A is technically correct for small datasets but is highly inefficient for trillions of rows, directly contradicting the "efficiently" requirement of the question.
* Option C is a distractor; while Snowflake has various SYSTEM$ functions, SYSTEM$ESTIMATE is not a standard function for cardinality.
* Option D is a formula that doesn't target cardinality but rather a ratio (density).
* Option B is the correct answer. The HLL family of functions is the industry standard within Snowflake for high-performance cardinality estimation on massive datasets.
NEW QUESTION # 35
You are tasked with preparing a large dataset of website clickstream data stored in a Snowflake table named This table contains a 'timestamp' column (TIMESTAMP NTZ), a column (VARCHAR), and a 'page_urr column (VARCHAR). You need to identify the most popular pages visited by users during specific hours of the day, but only for users who have visited at least 5 unique pages in the dataset. Which sequence of SQL operations, potentially including temporary tables or CTEs, would efficiently achieve this goal in Snowflake?
Answer: A,B
Explanation:
Option B and E are the most efficient solutions. Option B uses a CTE (Common Table Expression) to filter users who have visited at least 5 unique pages and then joins this result with the original table to calculate visit counts by hour and page. Option E creates a TEMP table for qualified users then joins against it to create a final aggregated set. A is functionally correct but may not be as optimized as using a CTE and uses two queries. C has an unnecessary subquery in the WHERE clause, which can degrade performance. D uses inline view which is less readable and less efficient than CTE. Both CTEs (B) and temporary tables (E) are common techniques in Snowflake for breaking down complex queries and can improve readability and maintainability. Snowflake's query optimizer is generally very good, and the CTE approach (B) is often preferred for its clarity.
NEW QUESTION # 36
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